scholarly journals Prediction of Alzheimer’s Disease Based on Coordinate-Dense Attention Network

2021 ◽  
Author(s):  
Yongmei Tang ◽  
Xiangyun Liao ◽  
Weixin Si ◽  
Zhigang Ning
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Brain Aging ◽  
Significant Feature ◽  
Convolutional Network ◽  
Basic Model ◽  
Prediction Scheme ◽  
Feature Information ◽  
The Brain

Alzheimer’s disease (AD) is a degenerative disease of the nervous system. Mild cognitive impairment (MCI) is a condition between brain aging and dementia. The prediction will be divided into stable sMCI and progressive pMCI as a binary task. Structural magnetic resonance imaging (sMRI) can describe structural changes in the brain and provide a diagnostic method for the detection and early prevention of Alzheimer’s disease. In this paper, an automatic disease prediction scheme based on MRI was designed. A dense convolutional network was used as the basic model. By adding a channel attention mechanism to the model, significant feature information in MRI images was extracted, and the unimportant features were ignored or suppressed. The proposed framework is compared with the most advanced methods, and better results are obtained.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

GeroPsych ◽  
2012 ◽  
Vol 25 (4) ◽  
pp. 235-245 ◽  
Author(s):  
Katja Franke ◽  
Christian Gaser
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Healthy Aging ◽  
Brain Aging ◽  
Elderly Subjects ◽  
Additional Increase ◽  
Longitudinal Changes ◽  
Novel Method ◽  
The Brain

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

scholarly journals The association between biomarkers in cerebrospinal fluid and structural changes in the brain in patients with Alzheimer's disease

2013 ◽  
Vol 275 (4) ◽  
pp. 418-427 ◽  
Author(s):  
X. Li ◽  
T.-Q. Li ◽  
N. Andreasen ◽  
M. K. Wiberg ◽  
E. Westman ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Structural Changes ◽  
The Brain

scholarly journals A Structural Parametrization of the Brain Using Hidden Markov Models-Based Paths in Alzheimer’s Disease

2016 ◽  
Vol 26 (07) ◽  
pp. 1650024 ◽  
Author(s):  
Francisco J. Martinez-Murcia ◽  
Juan M. Górriz ◽  
Javier Ramírez ◽  
Andres Ortiz
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Feature Reduction ◽  
Significant Feature ◽  
Computational Techniques ◽  
Occurrence Matrix ◽  
The Brain

The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called computed aided diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on hidden Markov models (HMMs). The path is traced using information of intensity and spatial orientation in each node, adapting to the structure of the brain. Each path is itself a useful way to characterize the distribution of the tissue inside the magnetic resonance imaging (MRI) image by, for example, extracting the intensity levels at each node or generating statistical information of the tissue distribution. Additionally, a further processing consisting of a modification of the grey level co-occurrence matrix (GLCM) can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to Alzheimer’s disease (AD), as well as providing a significant feature reduction. This methodology achieves moderate performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer’s disease neuroimaging initiative (ADNI).

scholarly journals Mechanisms of Brain Aging Regulation by Insulin: Implications for Neurodegeneration in Late-Onset Alzheimer's Disease

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Artur F. Schuh ◽  
Carlos M. Rieder ◽  
Liara Rizzi ◽  
Márcia Chaves ◽  
Matheus Roriz-Cruz
Keyword(s):  
Alzheimer’S Disease ◽  
Insulin Resistance ◽  
Alzheimer's Disease ◽  
Brain Aging ◽  
Late Onset ◽  
Energy Deficit ◽  
Insulin Degrading Enzyme ◽  
Microvascular Endothelium ◽  
Chronic Hyperglycemia ◽  
The Brain

Insulin and IGF seem to be important players in modulating brain aging. Neurons share more similarities with islet cells than any other human cell type. Insulin and insulin receptors are diffusely found in the brain, especially so in the hippocampus. Caloric restriction decreases insulin resistance, and it is the only proven mechanism to expand lifespan. Conversely, insulin resistance increases with age, obesity, and sedentarism, all of which have been shown to be risk factors for late-onset Alzheimer's disease (AD). Hyperphagia and obesity potentiate the production of oxidative reactive species (ROS), and chronic hyperglycemia accelerates the formation of advanced glucose end products (AGEs) in (pre)diabetes—both mechanisms favoring a neurodegenerative milieu. Prolonged high cerebral insulin concentrations cause microvascular endothelium proliferation, chronic hypoperfusion, and energy deficit, triggering β-amyloid oligomerization and tau hyperphosphorylation. Insulin-degrading enzyme (IDE) seems to be the main mechanism in clearing β-amyloid from the brain. Hyperinsulinemic states may deviate IDE utilization towards insulin processing, decreasing β-amyloid degradation.

scholarly journals Comparison of machine learning approaches for enhancing Alzheimer’s disease classification

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10549
Author(s):  
Qi Li ◽  
Mary Qu Yang
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Changes ◽  
Spatial Information ◽  
Neurodegenerative Disorder ◽  
Brain Regions ◽  
Disease Classification ◽  
Support Vector ◽  
The Brain

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, accounting for nearly 60% of all dementia cases. The occurrence of the disease has been increasing rapidly in recent years. Presently about 46.8 million individuals suffer from AD worldwide. The current absence of effective treatment to reverse or stop AD progression highlights the importance of disease prevention and early diagnosis. Brain structural Magnetic Resonance Imaging (MRI) has been widely used for AD detection as it can display morphometric differences and cerebral structural changes. In this study, we built three machine learning-based MRI data classifiers to predict AD and infer the brain regions that contribute to disease development and progression. We then systematically compared the three distinct classifiers, which were constructed based on Support Vector Machine (SVM), 3D Very Deep Convolutional Network (VGGNet) and 3D Deep Residual Network (ResNet), respectively. To improve the performance of the deep learning classifiers, we applied a transfer learning strategy. The weights of a pre-trained model were transferred and adopted as the initial weights of our models. Transferring the learned features significantly reduced training time and increased network efficiency. The classification accuracy for AD subjects from elderly control subjects was 90%, 95%, and 95% for the SVM, VGGNet and ResNet classifiers, respectively. Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to show discriminative regions that contributed most to the AD classification by utilizing the learned spatial information of the 3D-VGGNet and 3D-ResNet models. The resulted maps consistently highlighted several disease-associated brain regions, particularly the cerebellum which is a relatively neglected brain region in the present AD study. Overall, our comparisons suggested that the ResNet model provided the best classification performance as well as more accurate localization of disease-associated regions in the brain compared to the other two approaches.

Increased risk of Alzheimer's disease in Type II diabetes: insulin resistance of the brain or insulin-induced amyloid pathology?

2005 ◽  
Vol 33 (5) ◽  
pp. 1041-1044 ◽  
Author(s):  
G.J. Biessels ◽  
L.J. Kappelle
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Type Ii Diabetes ◽  
Brain Aging ◽  
Building Blocks ◽  
Type Ii ◽  
The Metabolic Syndrome ◽  
Increased Risk ◽  
The Brain

Type II diabetes mellitus (DM2) is associated with an increased risk of cognitive dysfunction and dementia. The increased risk of dementia concerns both Alzheimer's disease and vascular dementia. Although some uncertainty remains into the exact pathogenesis, several mechanisms through which DM2 may affect the brain have now been identified. First, factors related to the ‘metabolic syndrome’, a cluster of metabolic and vascular risk factors (e.g. dyslipidaemia and hypertension) that is closely linked to DM2, may be involved. A number of these risk factors are predictors of cerebrovascular disease, accelerated cognitive decline and dementia. Secondly, hyperglycaemia may be involved, through adverse effects of potentially ‘toxic’ glucose metabolites on the brain and its vasculature. Thirdly, insulin itself may be involved. Insulin can directly modulate synaptic plasticity and learning and memory, and disturbances in insulin signalling pathways in the periphery and in the brain have recently been implicated in Alzheimer's disease and brain aging. Insulin also regulates the metabolism of β-amyloid and tau, the building blocks of amyloid plaques and neurofibrillary tangles, the neuropathological hallmarks of Alzheimer's disease. In this paper, the evidence for the association between DM2 and dementia and for each of these underlying mechanisms will be reviewed, with emphasis on the role of insulin itself.

scholarly journals Cornel iridoid glycoside ameliorated Alzheimer's disease-like pathologies and necroptosis through RIPK1/MLKL pathway in young and aged SAMP8 mice

2021 ◽  
Author(s):  
Denglei Ma ◽  
Yanzheng Li ◽  
Yanqiu Zhu ◽  
Weipeng Wei ◽  
Li Zhang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Diseases ◽  
Brain Aging ◽  
Neuronal Loss ◽  
Iridoid Glycoside ◽  
Intragastric Administration ◽  
Cornel Iridoid Glycoside ◽  
Samp8 Mice ◽  
The Brain

Abstract Background Aging is an important risk factor for sporadic Alzheimer’s disease (AD) and other neurodegenerative diseases. Senescence-accelerated mouse-prone 8 (SAMP8) is used as an animal model for brain aging and sporadic AD researches. The aim of the current study was to investigate the pharmacological effects of cornel iridoid glycoside (CIG), an active ingredient of Cornus officinalis, on AD-type pathological changes in young and aged SAMP8 mice. Methods Nissl and immunohistochemical staining was applied to detect NeuN-labeled neurons and myelin basic protein-labeled myelin sheath,. Western blotting was used to detect the expression levels of related proteins of synapse, APP processing and necroptosis. Results The results showed that SAMP8 mice at the age of 6 and 14 months exhibited age-related neuronal loss, demyelination, synaptic damage, and APP amyloidogenic processing. In addition, the increased levels of receptor-interacting protein kinase-1 (RIPK1), mixed lineage kinase domain-like protein (MLKL) and p-MLKL indicating necroptosis were found in the brain of SAMP8 mice. Intragastric administration of CIG for 2 months alleviated neuronal loss and demyelination, increased the expression of synaptophysin, postsynaptic density protein 95 and AMPA receptor subunit 1, elevated the levels of soluble APPα fragment and a disintegrin and metalloproteinase 10 (ADAM10), and decreased the levels of RIPK1, p-MLKL and MLKL in the brain of young and aged SAMP8 mice. Conclusion This study denoted that CIG might be a potential drug for aging-associated neurodegenerative diseases such as AD.

scholarly journals Accelerated brain aging towards transcriptional inversion in a zebrafish model of familial Alzheimer’s disease

2018 ◽  
Author(s):  
Nhi Hin ◽  
Morgan Newman ◽  
Jan Kaslin ◽  
Alon M. Douek ◽  
Amanda Lumsden ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Stress Responses ◽  
Early Onset ◽  
Genetic Model ◽  
Brain Aging ◽  
Zebrafish Model ◽  
Oxidative Stresses ◽  
Brain Transcriptome ◽  
The Brain

AbstractAlzheimer’s disease (AD) develops silently over decades. We cannot easily access and analyse pre-symptomatic brains, so the earliest molecular changes that initiate AD remain unclear. Previously, we demonstrated that the genes mutated in early-onset, dominantly-inherited familial forms of AD (fAD) are evolving particularly rapidly in mice and rats. Fortunately, some non-mammalian vertebrates such as the zebrafish preserve fAD-relevant transcript isoforms of the PRESENILIN (PSEN1 and PSEN2) genes that these rodents have lost. Zebrafish are powerful vertebrate genetic models for many human diseases, but no genetic model of fAD in zebrafish currently exists. We edited the zebrafish genome to model the unique, protein-truncating fAD mutation of human PSEN2, K115fs. Analysing the brain transcriptome and proteome of young (6-month-old) and aged, infertile (24-month-old) wild type and heterozygous fAD-like mutant female sibling zebrafish supports accelerated brain aging and increased glucocorticoid signalling in young fAD-like fish, leading to a transcriptional ‘inversion’ into glucocorticoid resistance and vast changes in biological pathways in aged, infertile fAD-like fish. Notably, one of these changes involving microglia-associated immune responses regulated by the ETS transcription factor family is preserved between our zebrafish fAD model and human early-onset AD. Importantly, these changes occur before obvious histopathology and likely in the absence of Aβ. Our results support the contributions of early metabolic and oxidative stresses to immune and stress responses favouring AD pathogenesis and highlight the value of our fAD-like zebrafish genetic model for elucidating early changes in the brain that promote AD pathogenesis. The success of our approach has important implications for future modelling of AD.

Sign in / Sign up

Export Citation Format

Share Document